Evaluation of foundation models for medical image segmentation tasks
Open, modular MCP server for medical image analysis agents: containerized models (MedSAM2, SAM 2.1, TotalSegmentator, lungmask, HD-BET, SynthStrip, nnU-Net, MONAI, TorchXRayVision), preset/custom guidelines, and a code-customizable viewer.
PyTorch-based pipeline includes data preprocessing, model inference, and performance evaluation with standard metrics (Dice score, Hausdorff distance). The repository provides tools for visualizing segmentation results and comparing MedSAM-2's performance against baseline models, offering insights into adapting foundation models for medical imaging
TagMed is designed to facilitate the annotation of sensitive images and corresponding medical reports for computer vision projects.
AI-Powered Medical Imaging Analysis & Research Segmentation Platform
(LISA 2025 MICCAI Challange) Atlas-Augmented Semantic Segmentation for Robust Ultra-Low-Field Pediatric Brain Imaging
Video Object Labeling, User-guided Tracking and Extraction — Desktop GUI for annotating and tracking objects across video frames with SAM2, SAM2++ and MedSAM2, with batch processing and research-oriented exports. Designed for research in speech sciences but applicable to other fields.
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